THE ROLE
Drive AI strategy definition and implementation — identifying high-value use cases, setting up governance, and building actionable roadmaps.
Integrate AI tools (LLMs, code assistants, analysis tools) into existing development workflows, toolchains, and repositories.
Architect and support secure AI environments — on-premise solutions, access controls, and data-leak prevention with GDPR compliance at the core.
Build an internal knowledge AI — connecting systems such as Jira and DOORS to a chatbot layer with structured knowledge access.
Define AI policies, prompting standards, and best practices for LLM use in a developer context.
Enable development teams through hands-on workshops and alignment sessions — turning AI adoption from concept into daily practice.
WHAT YOU BRING — MUST-HAVES
Proven experience implementing AI solutions in enterprise environments.
Hands-on track record identifying and delivering concrete AI use cases (e.g. code assistance, analysis).
Experience integrating LLMs into existing development processes and toolchains (e.g. VS Code, repositories).
Strong understanding of AI-supported developer workflows (code review, analysis, assistance).
Solid knowledge of data protection (GDPR), corporate IP protection, and secure AI usage.
Experience with on-premise AI solutions, access restrictions, and controlled interfaces.
Know-how in handling sensitive data, repositories, and secrets/keys in code.
Experience with container technologies (e.g. Docker) and development environments.
Ability to create AI governance frameworks, policies, and best practices.
Experience enabling development teams and facilitating workshops.
NICE TO HAVE
Basic understanding of C++ and embedded environments.
Project experience in regulated or safety-critical environments.
Knowledge of RAG (Retrieval-Augmented Generation) and hallucination mitigation in LLMs.
Experience with knowledge-system integrations (e.g. Jira, DOORS, internal databases).
Experience building internal knowledge chatbots.